Overview

Dataset statistics

Number of variables30
Number of observations121
Missing cells119
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 KiB
Average record size in memory241.1 B

Variable types

Numeric11
Categorical19

Alerts

Unnamed: 0 is highly overall correlated with fev1High correlation
aecopd_12m is highly overall correlated with fev1High correlation
saturation is highly overall correlated with fev1High correlation
rr is highly overall correlated with fev1High correlation
sbp is highly overall correlated with dbp and 1 other fieldsHigh correlation
dbp is highly overall correlated with sbp and 1 other fieldsHigh correlation
hr is highly overall correlated with fev1High correlation
temperature is highly overall correlated with fev1High correlation
age is highly overall correlated with fev1High correlation
bmi is highly overall correlated with fev1High correlation
charlson is highly overall correlated with fev1 and 1 other fieldsHigh correlation
death is highly overall correlated with death_aecopd and 1 other fieldsHigh correlation
death_aecopd is highly overall correlated with death and 1 other fieldsHigh correlation
sex is highly overall correlated with fev1High correlation
season is highly overall correlated with fev1High correlation
oedema is highly overall correlated with fev1High correlation
retractions is highly overall correlated with fev1High correlation
confusion is highly overall correlated with fev1High correlation
dyspnoea_yesno is highly overall correlated with dyspnoea_mMRC and 1 other fieldsHigh correlation
dyspnoea_mMRC is highly overall correlated with dyspnoea_yesno and 1 other fieldsHigh correlation
fev1 is highly overall correlated with Unnamed: 0 and 28 other fieldsHigh correlation
rural is highly overall correlated with fev1High correlation
home_care is highly overall correlated with fev1High correlation
ami is highly overall correlated with fev1High correlation
heart_failure is highly overall correlated with fev1High correlation
cbd is highly overall correlated with fev1High correlation
pad is highly overall correlated with fev1High correlation
dementia is highly overall correlated with fev1High correlation
diabetes is highly overall correlated with fev1High correlation
cancer is highly overall correlated with charlson and 1 other fieldsHigh correlation
confusion is highly imbalanced (79.0%)Imbalance
dyspnoea_yesno is highly imbalanced (71.5%)Imbalance
home_care is highly imbalanced (61.8%)Imbalance
ami is highly imbalanced (58.9%)Imbalance
cbd is highly imbalanced (68.1%)Imbalance
pad is highly imbalanced (61.8%)Imbalance
dementia is highly imbalanced (71.5%)Imbalance
fev1 has 118 (97.5%) missing valuesMissing
fev1 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
aecopd_12m has 25 (20.7%) zerosZeros

Reproduction

Analysis started2023-04-05 17:33:21.642820
Analysis finished2023-04-05 17:33:34.761620
Duration13.12 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880.30579
Minimum1
Maximum1695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:34.839637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile64
Q1434
median886
Q31328
95-th percentile1633
Maximum1695
Range1694
Interquartile range (IQR)894

Descriptive statistics

Standard deviation511.45993
Coefficient of variation (CV)0.58100258
Kurtosis-1.2965578
Mean880.30579
Median Absolute Deviation (MAD)452
Skewness-0.021287433
Sum106517
Variance261591.26
MonotonicityNot monotonic
2023-04-05T13:33:34.939660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
926 1
 
0.8%
1393 1
 
0.8%
1390 1
 
0.8%
1379 1
 
0.8%
1350 1
 
0.8%
1328 1
 
0.8%
1274 1
 
0.8%
1264 1
 
0.8%
1261 1
 
0.8%
Other values (111) 111
91.7%
ValueCountFrequency (%)
1 1
0.8%
26 1
0.8%
37 1
0.8%
38 1
0.8%
49 1
0.8%
56 1
0.8%
64 1
0.8%
99 1
0.8%
149 1
0.8%
159 1
0.8%
ValueCountFrequency (%)
1695 1
0.8%
1694 1
0.8%
1691 1
0.8%
1690 1
0.8%
1663 1
0.8%
1655 1
0.8%
1633 1
0.8%
1623 1
0.8%
1616 1
0.8%
1612 1
0.8%

death
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
104 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 104
86.0%
1 17
 
14.0%

Length

2023-04-05T13:33:35.037682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:35.133264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 104
86.0%
1 17
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 104
86.0%
1 17
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104
86.0%
1 17
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104
86.0%
1 17
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104
86.0%
1 17
 
14.0%

death_aecopd
Categorical

Distinct2
Distinct (%)1.7%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
0.0
105 
1.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 105
86.8%
1.0 15
 
12.4%
(Missing) 1
 
0.8%

Length

2023-04-05T13:33:35.200826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:35.278843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 105
87.5%
1.0 15
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 225
62.5%
. 120
33.3%
1 15
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
66.7%
Other Punctuation 120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 225
93.8%
1 15
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 225
62.5%
. 120
33.3%
1 15
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 225
62.5%
. 120
33.3%
1 15
 
4.2%

sex
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
104 
0
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 104
86.0%
0 17
 
14.0%

Length

2023-04-05T13:33:35.344858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:35.422876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 104
86.0%
0 17
 
14.0%

Most occurring characters

ValueCountFrequency (%)
1 104
86.0%
0 17
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 104
86.0%
0 17
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 104
86.0%
0 17
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 104
86.0%
0 17
 
14.0%

season
Categorical

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
41 
3
34 
1
28 
2
18 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
0 41
33.9%
3 34
28.1%
1 28
23.1%
2 18
14.9%

Length

2023-04-05T13:33:35.488891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:35.571910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 41
33.9%
3 34
28.1%
1 28
23.1%
2 18
14.9%

Most occurring characters

ValueCountFrequency (%)
0 41
33.9%
3 34
28.1%
1 28
23.1%
2 18
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
33.9%
3 34
28.1%
1 28
23.1%
2 18
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41
33.9%
3 34
28.1%
1 28
23.1%
2 18
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41
33.9%
3 34
28.1%
1 28
23.1%
2 18
14.9%

aecopd_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7190083
Minimum0
Maximum6
Zeros25
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:36.013012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4561544
Coefficient of variation (CV)0.84708983
Kurtosis1.0526948
Mean1.7190083
Median Absolute Deviation (MAD)1
Skewness1.0460704
Sum208
Variance2.1203857
MonotonicityNot monotonic
2023-04-05T13:33:36.078027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 35
28.9%
2 34
28.1%
0 25
20.7%
3 14
 
11.6%
4 6
 
5.0%
6 4
 
3.3%
5 3
 
2.5%
ValueCountFrequency (%)
0 25
20.7%
1 35
28.9%
2 34
28.1%
3 14
 
11.6%
4 6
 
5.0%
5 3
 
2.5%
6 4
 
3.3%
ValueCountFrequency (%)
6 4
 
3.3%
5 3
 
2.5%
4 6
 
5.0%
3 14
 
11.6%
2 34
28.1%
1 35
28.9%
0 25
20.7%

saturation
Real number (ℝ)

Distinct27
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.719008
Minimum60
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:36.164047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile75
Q188
median92
Q393
95-th percentile97
Maximum99
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.686084
Coefficient of variation (CV)0.074522491
Kurtosis5.3048206
Mean89.719008
Median Absolute Deviation (MAD)2
Skewness-2.038006
Sum10856
Variance44.703719
MonotonicityNot monotonic
2023-04-05T13:33:36.248065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
92 23
19.0%
93 17
14.0%
91 10
 
8.3%
94 7
 
5.8%
95 7
 
5.8%
89 6
 
5.0%
88 6
 
5.0%
90 6
 
5.0%
98 5
 
4.1%
86 5
 
4.1%
Other values (17) 29
24.0%
ValueCountFrequency (%)
60 1
 
0.8%
62 1
 
0.8%
70 1
 
0.8%
72 1
 
0.8%
75 3
2.5%
76 1
 
0.8%
78 1
 
0.8%
80 3
2.5%
81 1
 
0.8%
82 1
 
0.8%
ValueCountFrequency (%)
99 1
 
0.8%
98 5
 
4.1%
97 2
 
1.7%
96 4
 
3.3%
95 7
 
5.8%
94 7
 
5.8%
93 17
14.0%
92 23
19.0%
91 10
8.3%
90 6
 
5.0%

rr
Real number (ℝ)

Distinct16
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.115702
Minimum12
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:36.334085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile15
Q122
median22
Q322
95-th percentile30
Maximum40
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.713792
Coefficient of variation (CV)0.21314231
Kurtosis4.3958096
Mean22.115702
Median Absolute Deviation (MAD)0
Skewness1.1926443
Sum2676
Variance22.219835
MonotonicityNot monotonic
2023-04-05T13:33:36.402101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
22 80
66.1%
15 6
 
5.0%
24 6
 
5.0%
20 4
 
3.3%
12 4
 
3.3%
28 4
 
3.3%
36 3
 
2.5%
18 3
 
2.5%
40 2
 
1.7%
16 2
 
1.7%
Other values (6) 7
 
5.8%
ValueCountFrequency (%)
12 4
 
3.3%
13 1
 
0.8%
14 1
 
0.8%
15 6
 
5.0%
16 2
 
1.7%
18 3
 
2.5%
20 4
 
3.3%
21 1
 
0.8%
22 80
66.1%
24 6
 
5.0%
ValueCountFrequency (%)
40 2
 
1.7%
36 3
 
2.5%
34 1
 
0.8%
30 2
 
1.7%
28 4
 
3.3%
26 1
 
0.8%
24 6
 
5.0%
22 80
66.1%
21 1
 
0.8%
20 4
 
3.3%

sbp
Real number (ℝ)

Distinct57
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.81818
Minimum77
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:36.495124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile100
Q1123
median135
Q3145
95-th percentile170
Maximum190
Range113
Interquartile range (IQR)22

Descriptive statistics

Standard deviation20.770973
Coefficient of variation (CV)0.15406656
Kurtosis0.4545202
Mean134.81818
Median Absolute Deviation (MAD)12
Skewness0.057737456
Sum16313
Variance431.43333
MonotonicityNot monotonic
2023-04-05T13:33:36.593146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 12
 
9.9%
140 8
 
6.6%
120 5
 
4.1%
132 5
 
4.1%
142 5
 
4.1%
130 5
 
4.1%
150 4
 
3.3%
115 4
 
3.3%
128 4
 
3.3%
110 3
 
2.5%
Other values (47) 66
54.5%
ValueCountFrequency (%)
77 1
 
0.8%
90 1
 
0.8%
92 1
 
0.8%
93 1
 
0.8%
96 2
1.7%
100 3
2.5%
104 1
 
0.8%
107 1
 
0.8%
109 1
 
0.8%
110 3
2.5%
ValueCountFrequency (%)
190 1
0.8%
186 1
0.8%
183 1
0.8%
181 1
0.8%
180 1
0.8%
172 1
0.8%
170 1
0.8%
165 1
0.8%
164 1
0.8%
163 2
1.7%

dbp
Real number (ℝ)

Distinct46
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.123967
Minimum30
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:36.693168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile48
Q161
median70
Q380
95-th percentile95
Maximum103
Range73
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.763218
Coefficient of variation (CV)0.19351027
Kurtosis-0.054626537
Mean71.123967
Median Absolute Deviation (MAD)10
Skewness0.0073421124
Sum8606
Variance189.42617
MonotonicityNot monotonic
2023-04-05T13:33:36.790191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
70 15
 
12.4%
60 12
 
9.9%
80 7
 
5.8%
75 6
 
5.0%
76 4
 
3.3%
71 4
 
3.3%
78 4
 
3.3%
64 4
 
3.3%
67 4
 
3.3%
54 3
 
2.5%
Other values (36) 58
47.9%
ValueCountFrequency (%)
30 1
 
0.8%
44 1
 
0.8%
46 2
1.7%
47 1
 
0.8%
48 3
2.5%
51 1
 
0.8%
52 2
1.7%
54 3
2.5%
55 1
 
0.8%
56 1
 
0.8%
ValueCountFrequency (%)
103 1
 
0.8%
102 1
 
0.8%
100 1
 
0.8%
96 1
 
0.8%
95 3
2.5%
94 1
 
0.8%
93 2
1.7%
90 3
2.5%
89 1
 
0.8%
88 2
1.7%

hr
Real number (ℝ)

Distinct58
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.305785
Minimum50
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:36.901216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q173
median83
Q399
95-th percentile115
Maximum155
Range105
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.050741
Coefficient of variation (CV)0.22073538
Kurtosis0.88584777
Mean86.305785
Median Absolute Deviation (MAD)12
Skewness0.72353323
Sum10443
Variance362.93072
MonotonicityNot monotonic
2023-04-05T13:33:37.005239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 6
 
5.0%
76 5
 
4.1%
82 5
 
4.1%
83 5
 
4.1%
75 4
 
3.3%
102 4
 
3.3%
72 4
 
3.3%
86 4
 
3.3%
109 4
 
3.3%
78 4
 
3.3%
Other values (48) 76
62.8%
ValueCountFrequency (%)
50 2
1.7%
51 1
0.8%
52 1
0.8%
56 1
0.8%
58 1
0.8%
60 1
0.8%
61 1
0.8%
63 2
1.7%
64 2
1.7%
66 1
0.8%
ValueCountFrequency (%)
155 1
0.8%
138 1
0.8%
132 1
0.8%
131 1
0.8%
128 1
0.8%
120 1
0.8%
115 2
1.7%
114 2
1.7%
112 1
0.8%
110 2
1.7%

temperature
Real number (ℝ)

Distinct35
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.98347
Minimum35
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:37.104262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36
Q1356
median362
Q3366
95-th percentile379
Maximum388
Range353
Interquartile range (IQR)10

Descriptive statistics

Standard deviation120.25279
Coefficient of variation (CV)0.3842145
Kurtosis1.6404438
Mean312.98347
Median Absolute Deviation (MAD)5
Skewness-1.8928035
Sum37871
Variance14460.733
MonotonicityNot monotonic
2023-04-05T13:33:37.192284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
362 35
28.9%
36 13
 
10.7%
365 8
 
6.6%
368 5
 
4.1%
355 5
 
4.1%
366 4
 
3.3%
35 3
 
2.5%
359 3
 
2.5%
373 3
 
2.5%
357 3
 
2.5%
Other values (25) 39
32.2%
ValueCountFrequency (%)
35 3
 
2.5%
36 13
10.7%
37 2
 
1.7%
38 1
 
0.8%
341 1
 
0.8%
347 1
 
0.8%
351 1
 
0.8%
353 2
 
1.7%
354 1
 
0.8%
355 5
 
4.1%
ValueCountFrequency (%)
388 2
1.7%
386 1
0.8%
385 1
0.8%
383 1
0.8%
381 1
0.8%
379 1
0.8%
377 2
1.7%
376 2
1.7%
375 2
1.7%
374 1
0.8%

oedema
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
89 
1.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89
73.6%
1.0 32
 
26.4%

Length

2023-04-05T13:33:37.285305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:37.368324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89
73.6%
1.0 32
 
26.4%

Most occurring characters

ValueCountFrequency (%)
0 210
57.9%
. 121
33.3%
1 32
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 210
86.8%
1 32
 
13.2%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 210
57.9%
. 121
33.3%
1 32
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 210
57.9%
. 121
33.3%
1 32
 
8.8%

retractions
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
103 
1.0
18 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 103
85.1%
1.0 18
 
14.9%

Length

2023-04-05T13:33:37.448342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:37.535362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 103
85.1%
1.0 18
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 224
61.7%
. 121
33.3%
1 18
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 224
92.6%
1 18
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 224
61.7%
. 121
33.3%
1 18
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 224
61.7%
. 121
33.3%
1 18
 
5.0%

confusion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
117 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 117
96.7%
1.0 4
 
3.3%

Length

2023-04-05T13:33:37.611379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:37.691397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 117
96.7%
1.0 4
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 238
65.6%
. 121
33.3%
1 4
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238
98.3%
1 4
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238
65.6%
. 121
33.3%
1 4
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238
65.6%
. 121
33.3%
1 4
 
1.1%

dyspnoea_yesno
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
115 
0.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 115
95.0%
0.0 6
 
5.0%

Length

2023-04-05T13:33:37.759413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:37.839431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 115
95.0%
0.0 6
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 127
35.0%
. 121
33.3%
1 115
31.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
52.5%
1 115
47.5%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127
35.0%
. 121
33.3%
1 115
31.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127
35.0%
. 121
33.3%
1 115
31.7%

dyspnoea_mMRC
Categorical

Distinct5
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
4.0
81 
2.0
15 
3.0
13 
1.0
 
6
0.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row1.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 81
66.9%
2.0 15
 
12.4%
3.0 13
 
10.7%
1.0 6
 
5.0%
0.0 6
 
5.0%

Length

2023-04-05T13:33:37.918448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:38.026473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 81
66.9%
2.0 15
 
12.4%
3.0 13
 
10.7%
1.0 6
 
5.0%
0.0 6
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 127
35.0%
. 121
33.3%
4 81
22.3%
2 15
 
4.1%
3 13
 
3.6%
1 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
52.5%
4 81
33.5%
2 15
 
6.2%
3 13
 
5.4%
1 6
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127
35.0%
. 121
33.3%
4 81
22.3%
2 15
 
4.1%
3 13
 
3.6%
1 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127
35.0%
. 121
33.3%
4 81
22.3%
2 15
 
4.1%
3 13
 
3.6%
1 6
 
1.7%

age
Real number (ℝ)

Distinct36
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.859504
Minimum52
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:38.119494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile62
Q171
median80
Q385
95-th percentile89
Maximum93
Range41
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.7371866
Coefficient of variation (CV)0.11221734
Kurtosis-0.2836894
Mean77.859504
Median Absolute Deviation (MAD)6
Skewness-0.67125147
Sum9421
Variance76.33843
MonotonicityNot monotonic
2023-04-05T13:33:38.210515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
80 12
 
9.9%
87 9
 
7.4%
83 8
 
6.6%
79 7
 
5.8%
85 7
 
5.8%
81 6
 
5.0%
88 5
 
4.1%
68 5
 
4.1%
84 4
 
3.3%
86 4
 
3.3%
Other values (26) 54
44.6%
ValueCountFrequency (%)
52 1
 
0.8%
58 1
 
0.8%
59 1
 
0.8%
60 2
1.7%
61 1
 
0.8%
62 2
1.7%
63 3
2.5%
64 1
 
0.8%
65 1
 
0.8%
66 2
1.7%
ValueCountFrequency (%)
93 1
 
0.8%
92 1
 
0.8%
90 3
 
2.5%
89 2
 
1.7%
88 5
4.1%
87 9
7.4%
86 4
3.3%
85 7
5.8%
84 4
3.3%
83 8
6.6%

fev1
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing118
Missing (%)97.5%
Memory size1.1 KiB
568.0
69.0
52.0

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Characters and Unicode

Total characters13
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row568.0
2nd row69.0
3rd row52.0

Common Values

ValueCountFrequency (%)
568.0 1
 
0.8%
69.0 1
 
0.8%
52.0 1
 
0.8%
(Missing) 118
97.5%

Length

2023-04-05T13:33:38.296535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:38.384554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
568.0 1
33.3%
69.0 1
33.3%
52.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
23.1%
0 3
23.1%
5 2
15.4%
6 2
15.4%
8 1
 
7.7%
9 1
 
7.7%
2 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
76.9%
Other Punctuation 3
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
30.0%
5 2
20.0%
6 2
20.0%
8 1
 
10.0%
9 1
 
10.0%
2 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3
23.1%
0 3
23.1%
5 2
15.4%
6 2
15.4%
8 1
 
7.7%
9 1
 
7.7%
2 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3
23.1%
0 3
23.1%
5 2
15.4%
6 2
15.4%
8 1
 
7.7%
9 1
 
7.7%
2 1
 
7.7%

bmi
Real number (ℝ)

Distinct73
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2734.2066
Minimum162
Maximum4043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:38.470574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile1931
Q12557
median2747
Q32973
95-th percentile3769
Maximum4043
Range3881
Interquartile range (IQR)416

Descriptive statistics

Standard deviation623.4246
Coefficient of variation (CV)0.22800932
Kurtosis6.4284072
Mean2734.2066
Median Absolute Deviation (MAD)205
Skewness-1.6584913
Sum330839
Variance388658.23
MonotonicityNot monotonic
2023-04-05T13:33:38.572597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2747 39
32.2%
2726 2
 
1.7%
3225 2
 
1.7%
3881 2
 
1.7%
2976 2
 
1.7%
2371 2
 
1.7%
1896 2
 
1.7%
3052 2
 
1.7%
2956 2
 
1.7%
2231 2
 
1.7%
Other values (63) 64
52.9%
ValueCountFrequency (%)
162 1
0.8%
264 1
0.8%
277 1
0.8%
426 1
0.8%
1896 2
1.7%
1931 2
1.7%
2081 1
0.8%
2218 1
0.8%
2231 2
1.7%
2239 1
0.8%
ValueCountFrequency (%)
4043 1
0.8%
3881 2
1.7%
3816 1
0.8%
3791 1
0.8%
3781 1
0.8%
3769 1
0.8%
3668 1
0.8%
3639 1
0.8%
3618 1
0.8%
3583 1
0.8%

rural
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
64 
1.0
57 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 64
52.9%
1.0 57
47.1%

Length

2023-04-05T13:33:38.665618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:38.745636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 64
52.9%
1.0 57
47.1%

Most occurring characters

ValueCountFrequency (%)
0 185
51.0%
. 121
33.3%
1 57
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 185
76.4%
1 57
 
23.6%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 185
51.0%
. 121
33.3%
1 57
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 185
51.0%
. 121
33.3%
1 57
 
15.7%

home_care
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
112 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 112
92.6%
1.0 9
 
7.4%

Length

2023-04-05T13:33:38.813652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:38.891669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112
92.6%
1.0 9
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 233
64.2%
. 121
33.3%
1 9
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 233
96.3%
1 9
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 233
64.2%
. 121
33.3%
1 9
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 233
64.2%
. 121
33.3%
1 9
 
2.5%

charlson
Real number (ℝ)

Distinct8
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4545455
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-04-05T13:33:38.954683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1447611
Coefficient of variation (CV)0.87379154
Kurtosis4.2049917
Mean2.4545455
Median Absolute Deviation (MAD)1
Skewness2.0860834
Sum297
Variance4.6
MonotonicityNot monotonic
2023-04-05T13:33:39.019699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 52
43.0%
2 34
28.1%
3 14
 
11.6%
4 8
 
6.6%
8 8
 
6.6%
7 3
 
2.5%
12 1
 
0.8%
6 1
 
0.8%
ValueCountFrequency (%)
1 52
43.0%
2 34
28.1%
3 14
 
11.6%
4 8
 
6.6%
6 1
 
0.8%
7 3
 
2.5%
8 8
 
6.6%
12 1
 
0.8%
ValueCountFrequency (%)
12 1
 
0.8%
8 8
 
6.6%
7 3
 
2.5%
6 1
 
0.8%
4 8
 
6.6%
3 14
 
11.6%
2 34
28.1%
1 52
43.0%

ami
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
111 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 111
91.7%
1.0 10
 
8.3%

Length

2023-04-05T13:33:39.093715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:39.172733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 111
91.7%
1.0 10
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 232
63.9%
. 121
33.3%
1 10
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 232
95.9%
1 10
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 232
63.9%
. 121
33.3%
1 10
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 232
63.9%
. 121
33.3%
1 10
 
2.8%

heart_failure
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
106 
1.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 106
87.6%
1.0 15
 
12.4%

Length

2023-04-05T13:33:39.239748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:39.318767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 106
87.6%
1.0 15
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 227
62.5%
. 121
33.3%
1 15
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 227
93.8%
1 15
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 227
62.5%
. 121
33.3%
1 15
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 227
62.5%
. 121
33.3%
1 15
 
4.1%

cbd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
114 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 114
94.2%
1.0 7
 
5.8%

Length

2023-04-05T13:33:39.383781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:39.460798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114
94.2%
1.0 7
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 235
64.7%
. 121
33.3%
1 7
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 235
97.1%
1 7
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 235
64.7%
. 121
33.3%
1 7
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235
64.7%
. 121
33.3%
1 7
 
1.9%

pad
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
112 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 112
92.6%
1.0 9
 
7.4%

Length

2023-04-05T13:33:39.524813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:39.603831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112
92.6%
1.0 9
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 233
64.2%
. 121
33.3%
1 9
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 233
96.3%
1 9
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 233
64.2%
. 121
33.3%
1 9
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 233
64.2%
. 121
33.3%
1 9
 
2.5%

dementia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
115 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 115
95.0%
1.0 6
 
5.0%

Length

2023-04-05T13:33:39.666845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:39.746420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 115
95.0%
1.0 6
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 236
65.0%
. 121
33.3%
1 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 236
97.5%
1 6
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 236
65.0%
. 121
33.3%
1 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 236
65.0%
. 121
33.3%
1 6
 
1.7%

diabetes
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
92 
1.0
29 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 92
76.0%
1.0 29
 
24.0%

Length

2023-04-05T13:33:39.811434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:39.891453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 92
76.0%
1.0 29
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 213
58.7%
. 121
33.3%
1 29
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 213
88.0%
1 29
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 213
58.7%
. 121
33.3%
1 29
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 213
58.7%
. 121
33.3%
1 29
 
8.0%

cancer
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
107 
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 107
88.4%
1.0 14
 
11.6%

Length

2023-04-05T13:33:39.959470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-05T13:33:40.040157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 107
88.4%
1.0 14
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 228
62.8%
. 121
33.3%
1 14
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228
94.2%
1 14
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228
62.8%
. 121
33.3%
1 14
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228
62.8%
. 121
33.3%
1 14
 
3.9%

Interactions

2023-04-05T13:33:33.342225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:24.554610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.403494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.432727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.301924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.112108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.926292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.762482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.825037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.666828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.477017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.417242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:24.648438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.482512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.512745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.377942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.196127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.004310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.840500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.904055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.741848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.557034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.490259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:24.722455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.553528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.590763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.450958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.270144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.078327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.915517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.980072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.812863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.637055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.568277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:24.806474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.633546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.673782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.528976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.348161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.159345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.996535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.062091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.909885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.724076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.639293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:24.877490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.703562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.749799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.598992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.426179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.231362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.065551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.136108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.981902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.799091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.708308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:24.951507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.774578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.823816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.669008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.494195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.303378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.134567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.213125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.051918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.874117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.785326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.028524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.848594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.904834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.750026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.569212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.380396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.208583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.293143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.126935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.956136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.854342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.101541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.919611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.978850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.818041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.637227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.452412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.534657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.366159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.193950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.031153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.930361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.180559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.997628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.063870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.898060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.714245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.530430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.610675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.443177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.268968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.114174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.996374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.249459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.283693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.141888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.964075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.779260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.602446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.676689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.512248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.331983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.186190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:34.075392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:25.331477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:26.363712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:27.226907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.043092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:28.858277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:29.684465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:30.756021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:31.595268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:32.410002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-05T13:33:33.268209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-05T13:33:40.134221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Unnamed: 0aecopd_12msaturationrrsbpdbphrtemperatureagebmicharlsondeathdeath_aecopdsexseasonoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCfev1ruralhome_careamiheart_failurecbdpaddementiadiabetescancer
Unnamed: 01.0000.0340.0100.0910.1230.1760.163-0.004-0.081-0.108-0.0560.2580.3230.3020.0000.0000.1930.1940.1760.0001.0000.0560.0000.0000.3130.0780.1340.3500.0000.000
aecopd_12m0.0341.000-0.043-0.015-0.055-0.1080.006-0.1720.177-0.134-0.0410.3620.2930.0450.0830.0000.0980.0000.0000.0001.0000.1120.0600.0000.1270.0000.0990.0000.0000.000
saturation0.010-0.0431.000-0.280-0.0720.019-0.343-0.0770.059-0.064-0.0080.3620.3910.0000.0000.1500.2620.4930.0000.1091.0000.0000.2440.0000.0000.0000.0000.0000.0150.074
rr0.091-0.015-0.2801.0000.022-0.0690.1090.0700.1590.040-0.1280.0000.0000.0000.0670.1170.4620.1070.1820.2681.0000.0000.0000.0000.0000.2800.0000.0880.2210.000
sbp0.123-0.055-0.0720.0221.0000.5850.180-0.118-0.1710.004-0.0840.1580.1850.1830.0000.1680.1740.0000.0000.0001.0000.0000.0000.0660.1860.2530.0000.0000.0000.280
dbp0.176-0.1080.019-0.0690.5851.0000.188-0.057-0.3280.091-0.2190.2290.2170.0000.0000.2000.3150.0000.0000.0001.0000.1480.0000.0000.1270.1650.0000.1640.1590.000
hr0.1630.006-0.3430.1090.1800.1881.0000.197-0.1800.0780.0110.1000.1750.0000.1220.1600.3750.0000.0000.0001.0000.0000.0000.0730.0000.0000.0000.3780.1820.258
temperature-0.004-0.172-0.0770.070-0.118-0.0570.1971.000-0.1720.002-0.0480.0000.0000.0000.1290.0000.0000.1190.0000.0001.0000.0950.0350.0000.0000.0000.0350.2940.0000.000
age-0.0810.1770.0590.159-0.171-0.328-0.180-0.1721.000-0.2150.0860.0000.0000.0000.0390.0000.1470.0000.0000.0001.0000.0000.0000.1920.1830.1920.1190.2370.0660.000
bmi-0.108-0.134-0.0640.0400.0040.0910.0780.002-0.2151.0000.0940.0000.0000.1030.0000.1190.0000.1390.0000.0471.0000.0000.0610.0820.1910.2710.0000.0550.1590.072
charlson-0.056-0.041-0.008-0.128-0.084-0.2190.011-0.0480.0860.0941.0000.0000.0000.0000.2220.0680.0000.0000.3380.2331.0000.1680.0000.3490.0000.4750.3200.2100.3160.914
death0.2580.3620.3620.0000.1580.2290.1000.0000.0000.0000.0001.0000.9260.0000.0000.0000.0000.0850.0000.1581.0000.0760.0000.0250.0420.0000.0000.0000.0000.000
death_aecopd0.3230.2930.3910.0000.1850.2170.1750.0000.0000.0000.0000.9261.0000.0000.0000.0000.0000.0000.0000.1271.0000.0000.0000.0680.0840.0000.0000.0000.0000.000
sex0.3020.0450.0000.0000.1830.0000.0000.0000.0000.1030.0000.0000.0001.0000.1220.0000.0000.0000.0000.0891.0000.0000.0650.1570.0000.0000.0000.0000.0000.000
season0.0000.0830.0000.0670.0000.0000.1220.1290.0390.0000.2220.0000.0000.1221.0000.1020.0000.0000.1400.1151.0000.0800.0000.0000.0000.0000.0000.0000.0930.000
oedema0.0000.0000.1500.1170.1680.2000.1600.0000.0000.1190.0680.0000.0000.0000.1021.0000.0000.1210.0220.1171.0000.0000.0000.0000.0000.0000.0000.0000.0000.092
retractions0.1930.0980.2620.4620.1740.3150.3750.0000.1470.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0120.0000.0000.0000.0000.0000.0000.0770.000
confusion0.1940.0000.4930.1070.0000.0000.0000.1190.0000.1390.0000.0850.0000.0000.0000.1210.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dyspnoea_yesno0.1760.0000.0000.1820.0000.0000.0000.0000.0000.0000.3380.0000.0000.0000.1400.0220.0000.0001.0000.9871.0000.0000.0000.0000.0000.1650.0000.0000.0000.195
dyspnoea_mMRC0.0000.0000.1090.2680.0000.0000.0000.0000.0000.0470.2330.1580.1270.0890.1150.1170.0000.0000.9871.0001.0000.2030.0000.0880.0000.2490.0000.0000.1130.245
fev11.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
rural0.0560.1120.0000.0000.0000.1480.0000.0950.0000.0000.1680.0760.0000.0000.0800.0000.0120.0000.0000.2031.0001.0000.0000.0000.2040.1270.0000.0900.2220.120
home_care0.0000.0600.2440.0000.0000.0000.0000.0350.0000.0610.0000.0000.0000.0650.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.2010.0000.0000.000
ami0.0000.0000.0000.0000.0660.0000.0730.0000.1920.0820.3490.0250.0680.1570.0000.0000.0000.0000.0000.0881.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
heart_failure0.3130.1270.0000.0000.1860.1270.0000.0000.1830.1910.0000.0420.0840.0000.0000.0000.0000.0000.0000.0001.0000.2040.0000.0001.0000.0000.0000.0000.0830.000
cbd0.0780.0000.0000.2800.2530.1650.0000.0000.1920.2710.4750.0000.0000.0000.0000.0000.0000.0000.1650.2491.0000.1270.0000.0000.0001.0000.0000.0000.0000.285
pad0.1340.0990.0000.0000.0000.0000.0000.0350.1190.0000.3200.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.2010.0000.0000.0001.0000.0000.0000.000
dementia0.3500.0000.0000.0880.0000.1640.3780.2940.2370.0550.2100.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0900.0000.0000.0000.0000.0001.0000.0000.000
diabetes0.0000.0000.0150.2210.0000.1590.1820.0000.0660.1590.3160.0000.0000.0000.0930.0000.0770.0000.0000.1131.0000.2220.0000.0000.0830.0000.0000.0001.0000.000
cancer0.0000.0000.0740.0000.2800.0000.2580.0000.0000.0720.9140.0000.0000.0000.0000.0920.0000.0000.1950.2451.0000.1200.0000.0000.0000.2850.0000.0000.0001.000

Missing values

2023-04-05T13:33:34.217424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-05T13:33:34.537756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-05T13:33:34.701606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0deathdeath_aecopdsexseasonaecopd_12msaturationrrsbpdbphrtemperatureoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCagefev1bmiruralhome_carecharlsonamiheart_failurecbdpaddementiadiabetescancer
0100.013192.026.0124.062.069.035.01.00.00.01.02.085.0NaN2917.00.00.04.00.00.00.00.01.01.00.0
12600.013087.020.0147.082.0102.036.01.00.00.01.04.083.0NaN3241.00.00.03.01.00.00.00.00.00.00.0
23700.010394.022.0115.075.058.0362.00.00.00.01.01.081.0NaN2747.00.00.01.00.00.00.00.00.00.00.0
33800.013292.015.0130.075.082.0362.00.00.00.00.00.081.0NaN2747.00.00.01.00.00.00.00.00.00.00.0
44900.010092.022.0143.060.068.0362.00.00.00.01.02.074.0NaN2635.00.00.02.00.00.00.01.00.00.00.0
56400.010070.022.0123.046.051.0362.01.00.01.01.02.079.0NaN426.01.00.01.00.00.00.00.00.00.00.0
69900.013089.040.0165.095.0114.0381.00.00.00.01.04.080.0NaN2747.01.01.01.00.00.00.00.00.00.00.0
714900.011187.022.0145.071.090.0365.01.00.00.01.04.066.0NaN3668.00.00.02.00.00.00.00.00.01.00.0
815900.013193.022.0120.051.061.0373.00.00.00.01.04.090.0NaN2747.00.00.01.00.00.00.00.00.00.00.0
922000.010083.022.0158.088.076.0366.00.01.00.01.04.068.0NaN3105.01.00.07.00.00.00.00.00.00.01.0
Unnamed: 0deathdeath_aecopdsexseasonaecopd_12msaturationrrsbpdbphrtemperatureoedemaretractionsconfusiondyspnoea_yesnodyspnoea_mMRCagefev1bmiruralhome_carecharlsonamiheart_failurecbdpaddementiadiabetescancer
1117191NaN11495.022.0135.070.068.0365.00.00.00.01.04.090.0NaN2747.01.00.01.00.00.00.00.00.00.00.0
112118711.012492.022.0135.070.083.0362.00.00.00.01.04.076.0NaN2747.01.01.02.00.00.01.00.00.00.00.0
113130811.011092.022.0135.070.083.0362.00.00.00.01.04.081.0NaN2747.01.00.04.01.00.00.00.00.00.01.0
114132411.013292.022.0140.060.0110.0362.01.00.00.01.04.079.0NaN2338.00.00.01.00.00.00.00.00.00.00.0
115138011.011175.022.077.044.0115.0362.00.01.00.01.04.077.0NaN2747.01.00.01.00.00.00.00.00.00.00.0
116160111.011276.022.0130.060.072.0362.00.00.00.01.04.086.0NaN2218.00.00.04.00.00.00.00.00.01.01.0
117160911.013175.022.0115.069.086.0356.00.00.01.01.04.087.0NaN2747.00.00.01.00.00.00.00.00.00.00.0
118163311.013295.022.0135.070.078.0362.00.00.00.01.04.080.0NaN2747.01.00.08.00.01.00.00.00.00.01.0
119165511.013391.022.0135.070.088.0362.00.00.00.01.03.081.0NaN2747.00.01.03.01.00.00.01.00.00.00.0
120169411.010292.024.0110.067.056.0375.01.00.00.01.04.084.0NaN2747.01.00.02.00.01.00.00.00.00.00.0